PaddleSpeech/examples/librispeech/asr0

DeepSpeech2 offline/online ASR with Librispeech

This example contains code used to train a DeepSpeech2 offline or online model with [Librispeech dataset](http://www.openslr.org/resources/33)

Overview

All the scirpts you need are in the run.sh. There are several stages in the run.sh, and each stage has its function.

Stage	Function
0	Process data. It includes: (1) Download the dataset (2) Caculate the CMVN of the train dataset (3) Get the vocabulary file (4) Get the manifest files of the train, development and test dataset
1	Train the model
2	Get the final model by averaging the top-k models, set k = 1 means choose the best model
3	Test the final model performance
4	Export the static graph model
5	Test the static graph model
6	Infer the single audio file

You can choose to run a range of stages by setting the stage and stop_stage .

For example, if you want to execute the code in stage 2 and stage 3, you can run this script:

bash run.sh --stage 2 --stop_stage 3

Or you can set stage equal to stop-stage to only run one stage. For example, if you only want to run stage 0, you can use the script below:

bash run.sh --stage 0 --stop_stage 0

The document below will describe the scripts in the run.sh in detail.

The environment variables

The path.sh contains the environment variable.

source path.sh

This script needs to be run firstly.

And another script is also needed:

source ${MAIN_ROOT}/utils/parse_options.sh

It will support the way of using--varibale value in the shell scripts.

The local variables

Some local variables are set in the run.sh. gpus denotes the GPU number you want to use. If you set gpus=, it means you only use CPU.

stage denotes the number of stage you want to start from in the expriments. stop stagedenotes the number of stage you want to end at in the expriments.

conf_path denotes the config path of the model.

avg_num denotes the number K of top-K models you want to average to get the final model. model_typedenotes the model type: offline or online audio file denotes the file path of the single file you want to infer in stage 6

ckpt denotes the checkpoint prefix of the model, e.g. "deepspeech2"

You can set the local variables (except ckpt) when you use the run.sh

For example, you can set the gpus and avg_num when you use the command line.:

bash run.sh --gpus 0,1 --avg_num 1

Stage 0: Data processing

To use this example, you need to process data firstly and you can use stage 0 in the run.sh to do this. The code is shown below:

 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
     bash ./local/data.sh || exit -1
 fi

Stage 0 is for processing the data.

If you only want to process the data. You can run

bash run.sh --stage 0 --stop_stage 0

You can also just run these scripts in your command line.

source path.sh
bash ./local/data.sh

After processing the data, the data directory will look like this:

data/
|-- dev.meta
|-- lang_char
|   `-- vocab.txt
|-- manifest.dev
|-- manifest.dev.raw
|-- manifest.test
|-- manifest.test.raw
|-- manifest.train
|-- manifest.train.raw
|-- mean_std.json
|-- test.meta
`-- train.meta

Stage 1: Model training

If you want to train the model. you can use stage 1 in the run.sh. The code is shown below.

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt}
 fi

If you want to train the model, you can use the script below to execute stage 0 and stage 1:

bash run.sh --stage 0 --stop_stage 1

or you can run these scripts in the command line (only use CPU).

source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2

Stage 2: Top-k Models Averaging

After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:

 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # avg n best model
     avg.sh best exp/${ckpt}/checkpoints ${avg_num}
 fi

The avg.sh is in the ../../../utils/ which is define in the path.sh. If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:

bash run.sh --stage 0 --stop_stage 2

or you can run these scripts in the command line (only use CPU).

source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2
avg.sh best exp/deepspeech2/checkpoints 1

Stage 3: Model Testing

The test stage is to evaluate the model performance.. The code of test stage is shown below:

 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi

If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :

bash run.sh --stage 0 --stop_stage 3

or you can run these scripts in the command line (only use CPU).

source path.sh
bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2
avg.sh best exp/deepspeech2/checkpoints 1
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1

Stage 4: Static graph model Export

This stage is to transform the dynamic graph model to static graph model.

 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # export ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}
 fi

If you already have a dynamic graph model, you can run this script:

source path.sh
./local/export.sh deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 exp/deepspeech2/checkpoints/avg_1.jit offline

Stage 5: Static graph Model Testing

Similer to stage 3, static graph model can also be tested.

 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test export ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
 fi

If you already have export the static graph, you can run this script:

CUDA_VISIBLE_DEVICES= ./local/test_export.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1.jit offline

Stage 6: Single Audio File Inference

In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below

if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     # test a single .wav file
     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file}
 fi

You can downloads the audio demo:

wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/

You can train a model by yourself, then you need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of audio demo by running the script below.

CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 data/demo_002_en.wav